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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： S. Jayaprakash T. Kanthimathi N. Rathika P. Sathyanathan C. Srinivasan Associate Professor School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Electrical and Electronics Engineering Mahendra Institute of Technology Tiruchengode Tamil Nadu India Department of Electrical and Electronics Engineering VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

There has been a lot of focus on the potential benefits to the industry of combining Renewable Energy Sources (RES) with Internet of Things (IoT) technology. The conventional motor-driven control system often uses traditional power sources and has poor continuous tracking capabilities, which limits its energy economy and control accuracy. This research introduces a unique method for enhanced energy efficiency and control by integrating a fuzzy algorithm, a photovoltaic (PV) power source, IoT-monitored four-switch induction motor (IM) drive. A fuzzy algorithm is used to improve the motor drive system. According to input factors like torque, load conditions and speed, the fuzzy logic controller (FLC) modifies the motor driving settings. Because of the adaptive properties of the fuzzy algorithm, the IM may be controlled effectively, improving system performance. Additionally, the IoT monitoring feature allows for remote control and real-time data collecting, improving system performance and issue detection. MATLAB/Simulink is employed to demonstrate the obtained results of the developed system. The results of this research may be used in a variety of fields, including robots, electric cars, and industrial automation powered by renewable energy.

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The Critical Role Played by Big Data Management in Effectively Addressing the Security and Overall Privacy Concerns Through Correlation Analysis 2

The Critical Role Played by Big Data Management in Effective...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Ahamad, Shahanawaj Roshan, Amit Lourens, Melanie Shekher, Vineet Joshi, Kapil Alanya-Beltran, Joel College of Computer Science and Engineering University of Hail Saudi Arabia Information Technology Dr Ram Manohar Lohiya Avadh University UP Ayodhya India Durban University of Technology Department of Human Resources Management South Africa Bit Sindri Electrical Engineering Department Dhanbad India UIt Uttaranchal University India Universidad Tecnologica DelPeru Professor. Electronic Department Peru

ISBN: (纸本)9781665437899

Due to the widespread application of big data in a wide range of fields, there has been a significant rise in a diverse range of data assets, and numerous data analysis technologies, such as standardized data mining or quantitative analytical techniques, are speeding up the constant enlargement of the big data industry. The fact that information from several different resources has a life cycle from acquisition to disposal is a key property of big data, as is the fact that new information may be obtained via analysis, combining, and exploitation of existing data. Yet, information security & dependability challenges arise at every stage of the life cycle, rendering the protection of individually identifying data a vital goal throughout the whole process. It is possible to evaluate user behavior using different big data analytics techniques, and the collection of this information results in the violation of personal privacy. This study analyses dangers and security challenges that arise during the administration of large amounts of data by verifying the existing standards produced by international standardization bodies and doing an analysis of relevant research. © 2022 IEEE.

关键词： Big data

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E-Buddy for Rescue Child Labour

E-Buddy for Rescue Child Labour

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BIT Conference (BITCON), International

作者： Ajmeera Kiran Prithiviraj Ananth P Veera Prakash N. Kumareshan Ramesh Kumar Ayyasamy P. Chinnasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of CSE Sona College of Technology Salem Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Ananthapuramu Andhra Pradesh Department of ECE Professor/Centre for Advances in Signal Processing and Sensor Networks Sri Eshwar College of Engineering Coimbatore Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

Ebuddy is a ground breaking platform dedicated to rescuing and rehabilitating child laboures. By leveraging advanced technology, this initiative effectively connects with at-risk children, providing them with crucial education and support services. With a mission to break the vicious cycle of poverty and exploitation, Ebuddy collaborates with local communities and organizations to raise awareness about child labor and advocate for policies that safeguard children's rights. This platform offers comprehensive support services aimed at addressing the children's physical, emotional, and psychological well-being. By providing counseling, rehabilitation, and necessary medical assistance, Ebuddy empowers children to leave their past behind and embrace a better future.

关键词： Employee welfare Pediatrics Education Psychology Morphology Organizations

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A Novel Forward Filter Feature Selection Algorithm Based on Maximum Dual Interaction and Maximum Feature Relevance(MDIMFR) for Machine Learning

A Novel Forward Filter Feature Selection Algorithm Based on ...

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International Conference on Advanced computer Control, ICACC

作者： M A Anitha K K Sherly Assistant Professor of Computer Science College of Engineering Cherthala Kerala India Associate Professor of Information Technology Rajagiri School of Engineering & Technology Kerala India

ISBN: (纸本)9781665439206

In the last few decades, Feature selection is one of the most challenging and open problem to researchers. The rapid progress in computational techniques causes the generation and recording of data in huge size. Though there exists various feature ranking methods, the processing of data is still a challenging task due to its computational complexity. The filter method has many advantages over the wrapper method. The filter methods are classifier independent and have better computational efficiency. Here, a subset of features is selected based on a certain goal function. Most of these goal functions employs the principle of information theory. Most of the algorithms in earlier studies addressed two factors, that is, maximization of relevancy and minimization of redundancy without considering the interaction among the features. This paper developed a new forward filter feature selection algorithm based on mutual information known as Maximum Dual Interaction and Maximum Feature Relevance(MDIMFR). This method considers all the three factors: relevance, redundancy, and feature interaction. This method is experimented on three datasets and compares the performance with existing methods. The results show that MDIMFR outperforms the existing competitive feature selection methods of recent studies: mRMR, JMIM and CMIM. MDIMFR also achieves good stability in average classification accuracy for a certain number of features, say k and above. Hence, these k features can be considered as an optimal feature set.

关键词： Machine learning algorithms Redundancy Filtering algorithms Feature extraction Information filters Minimization Filtering theory

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Analysis of Barriers to Industry 4.0 adoption in Manufacturing Organizations: an ISM Approach

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Procedia CIRP 2021年 98卷 85-90页

作者： Pramod Kumar Jaiprakash Bhamu Kuldip Singh Sangwan Research Scholar Department of Mechanical Engineering Engineering College Bikaner Rajasthan India Associate Professor Department of Mechanical Engineering Engineering College Biakner Rajasthan India Professor Department of Mechanical Engineering Birla Institute of Technology and Science Pilani Rajasthan India

Industry 4.0 has enabled technological integration of cyber physical systems and internet based communication in manufacturing value creation processes. As of now, many people use it as a collective term for advanced technologies, i.e. advanced robotics, artificial intelligence, machine learning, big data analytics, cloud computing, smart sensors, internet of things, augmented reality, etc. This substantially improves flexibility, quality, productivity, cost, and customer satisfaction by transforming existing centralized manufacturing systems towards digital and decentralized one. Despite having potential benefits of industry 4.0, the organizations are facing typical obstacles and challenges in adopting new technologies and successful implementation in their business models. This paper aims to identify potential barriers which may hinder the implementation of industry 4.0 in manufacturing organizations. The identified barriers, through comprehensive literature review and on the basis of opinions collected from industry experts, are: poor value-chain integration, cyber-security challenges, uncertainty about economic benefits, lack of adequate skills in workforce, high investment requirements, lack of infrastructure, jobs disruptions, challenges in data management and data quality, lack of secure standards and norms, and resistance to change. Interpretive Structural Modeling (ISM) is used to establish relationships among these barriers to develop a hierarchical model and MICMAC analysis for further classification of identified barriers for better understanding. An analysis of driving and dependence of the barriers may help in clear understanding of these for successful implementation of Industry 4.0 practices in the organizations.

关键词： Industry 4.0 interpretive structural modeling digital manufacturing barriers MICMAC analysis

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Influence of Lung Cancer Model Characteristics on Nanodrugs Tumor Targeting Behavior Based on Convolutional Kernel Extreme Learning Machine Model

Influence of Lung Cancer Model Characteristics on Nanodrugs ...

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Advancement in Technology (ICONAT), International Conference for

作者： Manthan S. Manavadaria Srikanth Chintakindi Rajasree Rajamohanan S Krishna Prasad P. Janardhan Saikumar Neerav Nishant EC Department CSPIT CHARUSAT Changa Petlad Gujarat India Associate Project Manager Cloud Software Group Department of Computer Science (Artificial intelligence and Data Science) Karpagam Academy of Higher Education Coimbatore India Department of Mechanical Engineering Nitte (Deemed to be University) NMAM Institute of Technology Nitte Udupi India Department of ECE Audisankara College of Engineering and Technology Gudur India Department of CSE School of Engineering Babu Banarasi Das University Lucknow India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

One of the main killers on a worldwide basis is cancer. In terms of global persistence, lung cancer stands out among the most frequent cancers. For lung cancer, the standard medical approach includes chemotherapy, surgical removal, and radiation therapy. When compared to other methods, this one isn’t extremely targeted and may even damage surrounding healthy cells. There has been recent recognition of nanotechnology as a possible tool for the treatment and management of lung cancer. Preprocessing, feature selection, segmentation, and model training must be executed in this precise sequence to ensure accuracy. As part of the preprocessing, the proposed approach employ the kernel correlation approach. Splitting the input image into smaller pieces with the goal of reducing the overall representation of the image is called image segmentation. To find features with high predictive power and remove redundant ones, feature selection was done in the training cohort. The model was trained using a Convolutional-KELM. As compared to CNN and ELM, the suggested approach performs better. Following implementation of the technique, the accuracy increased by 94.25%.

关键词： Training Image segmentation Accuracy Lungs Lung cancer Feature extraction Convolutional neural networks Kernel Medical diagnostic imaging Tumors

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A Deep Learning Aided Intelligent Framework for Condition Monitoring of Electrical Machinery

A Deep Learning Aided Intelligent Framework for Condition Mo...

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Devices for Integrated Circuit (DevIC)

作者： Biswarup Ganguly Radha Kumari Ray Arunava Chatterjee Subho Paul Electrical Engineering Department Meghnad Saha Institute of Technology Kolkata India Application Development Associate Accenture Kolkata India Electrical Engineering Department Raghunathpur Govt. Polytechnic Purulia India School of Electrical Engineering and Computer Science Washington State University Pullman WA USA

Single-phase induction motors (SPIM) are fairly efficiently employed in household devices and possess wide applications in the industrial system. Typically, current signature-analysis (CSA) based methods have been used for fault diagnosis, but not always been an efficient solution, especially for fractional kW SPIMs that may be replaced entirely iffaulty. This paper presents a deep learning (DL) based fault detection scheme of a SPIM which is in-service in household devices. The fault detection is done employing a wavelet based deep dense convolutional neural network (W-D2CNN), where several wavelets are used as convolutional filters for convolution operation. The convoluted feature maps are further passed through a proposed nonlinearity, i.e., SLReLU, for richer feature extraction. The uniqueness of the proposed method lies in the feature learning ability, where upper-and lower-layer features of the dense block fuse via concatenation operation. Also, the SL-ReLU has been designed to combine logarithmic function, softsign, and ReLU operation. The designed SL-ReLU minimizes the probability of convoluted output and gradient explosion, due to which training has been improved along with feature learning ability. Comprehensive results reveal the efficacy of the proposed work in terms of performance metrics with state-of-the-art DL frameworks using the same dataset. The proposed work can also be extendable to multiphase-induction machines or any other electrical device working in any environment for fault identification.

关键词： Representation learning Fault diagnosis Training Deep learning Fault detection Feature extraction Explosions

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Virtual 3D Game-on simulation: An immersive learning framework for assisted driving

Virtual 3D Game-on simulation: An immersive learning framewo...

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2022 International Conference for Advancement in Technology, ICONAT 2022

作者： Johnson, Janet Mary Joy, Geethu Rose Sindhu, Y Joy, Emmanuel Digital Marketing Associate 10xDS- Exponential Digital Solution Trivandrum India Developer - Software Engineer UST Global Digital Transformation Solutions Bangalore India Karunya Institute of Technology and Sciences Department of Electronics and Media Coimbatore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665425773

This paper demonstrates the design and architecture of a virtual driving simulation game. Virtual reality is an alternative world filled with computer-generated images that respond to human movements. Thus, early learners can drive without facing the real-world consequences. The outcome of this research is that it helps early driving learners to overcome the fear of driving. Early learners can easily get over panic and unwanted anxiety. The theoretical framework of this driving game simulation has been designed by using Unity 3D game engine and C# scripting. Gaming controls are given to the player either through the keyboard or through a joystick. The game is visually seen on the monitor screen and headsets are used for achieving realism. Maya and Blender software was used for modelling the framework of the game. We have achieved the output controls of the game by interfacing custom-designed electronic components such as the steering and the accelerator. The goal of this game is to create a virtual learning environment in which early learners can feel at ease while playing. The outcome of this game is to achieve self-confidence among the early learners that help them take away the fear built within them. Even though more women are entering the workforce, there is still a vulnerable segment of the population that is fearful of driving. This game may assist them in overcoming their anxiety and fear. © 2022 IEEE.

关键词： Virtual reality

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Optimizing Hindi Paragraph Summarization through PageRank Method

Optimizing Hindi Paragraph Summarization through PageRank Me...

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International Conference on Edge Computing and Applications (ICECAA)

作者： Sheshang Degadwala Dhairya Vyas Khush Nilaykumar Patel Mukesh Soni Pavitar Parkash Singh Ramya Maranan Associate Professor & Head of Computer Engineering Department Sigma University Vadodara Gujarat India The Maharaja Sayajirao University of Baroda Vadodara Gujarat India Charotar University of Science and Technology Anand Gujarat Department of CSE University Centre for Research & Development Chandigarh University Mohali Punjab India Lovely Professional University Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

This research paper presents a novel approach to optimize Hindi paragraph summarization using the PageRank method. With the increasing volume of Hindi language content on the internet, there is a growing need for effective summarization techniques tailored specifically for Hindi text. The proposed method leverages the inherent structure and connectivity of Hindi paragraphs to extract the most important sentences for summarization. The PageRank algorithm is applied to construct a rank representation of the text, with sentences as nodes and their co-occurrence patterns as edges. By calculating PageRank scores iteratively, the algorithm identifies the most central and significant sentences in the paragraph. Linguistic features such as sentence length, position, and content relevance are incorporated to enhance the quality of the summaries. Additionally, the algorithm dynamically adjusts sentence importance based on their relations with other sentences rank. Experimental results on a comprehensive dataset demonstrate that the PageRank-based approach outperforms existing summarization techniques in terms of informativeness and coherence, as evidenced by higher similarity score. The proposed method offers an effective solution for optimizing Hindi paragraph summarization, facilitating the management and comprehension of Hindi text in various applications.

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The Technical Ethics used in Bad and SD Methods: A Deep Review

The Technical Ethics used in Bad and SD Methods: A Deep Revi...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Aadithyan Ammar Hameed Shnain Vikrant Sharma Alaa M. Lafta Mustafa Mudhafar Ali Ghobash Ahmed Qusay Jawad Department of computer applications Hindustan institute of technology and science Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of CSE Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Department of Medical Physics Faculty of Mediscal Applied Sciences University of Kerbala Karbala Iraq Computer Engineer Department Al-kunooze University College Basra Iraq Departmen of Electrical Engineering Technologies Kut University College/Wasit Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Artificial intelligence (AI) is the process by which human-designed systems analyse available data and make judgements based on predetermined criteria in order to accomplish particular objectives. The incorporation of artificial intelligence (AI) has sparked a deep ethical conversation among key players, including tech companies, businesses, educational institutions, and legislators. AI’s ethical problems have prompted the creation of ethical frameworks that aim to control its application for the good of society, the defence of human rights, and the maintenance of individual autonomy and privacy. This paper provides a critical evaluation and summary of the fundamental concepts behind the use of artificial intelligence, with a focus on the GDPR’s (General Data Protection Regulation) approach. The rights of data subjects, data protection legislation, and data subjects’ permission are crucial considerations in the context of “privacy by design.”

关键词： Ethics Privacy Reviews Oral communication Companies Systems engineering and theory Maintenance

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